BMIKE-53: Investigating Cross-Lingual Knowledge Editing with In-Context Learning

This paper introduces BMIKE-53, a comprehensive benchmark for cross-lingual in-context knowledge editing (IKE) across 53 languages, unifying three knowledge editing (KE) datasets: zsRE, CounterFact, and WikiFactDiff.Crosslingual KE, which requires knowledge edited in one language to generalize across others while preserving unrelated knowledge, remains underexplored.To address this gap, we systematically evaluate IKE under zero-shot, oneshot, and few-shot setups, incorporating tailored metric-specific demonstrations.Our findings reveal that model scale and demonstration alignment critically govern cross-lingual IKE efficacy, with larger models and tailored demonstrations significantly improving performance.Linguistic properties, particularly script type, strongly influence performance variation across languages, with non-Latin languages underperforming due to issues like language confusion.Code and data are publicly available at: https: //github.com/ercong21/MultiKnow/.